Magnetic Inertial Measurement Units Research Articles

UWB distance estimation errors in (non-)line of sight situations within the context of 3D analysis of human movement

Abstract Integrated Ultrawideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensors are becoming popular for indoor localization applications, as a higher accuracy can be achieved than with just MIMU sensors. These integrated sensors could extend stability and accuracy in the field of 3D analysis of human movement (3D AHM) if they can deliver position estimates with an accuracy close to 1 cm. Achieving this high accuracy of 1 cm remains challenging, with most studies reporting position estimation errors around 5 cm, often due to Non-Line of Sight (NLOS) conditions and systematic UWB sensor distance estimation errors. Studying the distance error characteristic of UWB in situations of 3D AHM is essential to deal with these errors. While research on UWB distance errors in Line of Sight (LOS) and NLOS situations exists, few studies focus on the NLOS errors caused by the human body and were not in the relevant scenarios of 3D AHM. Therefore, this article examines UWB sensor performance and distance error characteristics in LOS and NLOS situations typical for the 3D AHM. Both the LOS and NLOS situations were studied in the typical 3D AHM distance range of 0.2 m to 2 m. The NLOS situations were studied first with a human subject as NLOS causing object and then with simulated human body segments (PVC pipes filled with water) of varying diameters. In LOS situations, consistent systematic bias errors were observed, along with incidental errors at specific positions in the room. In NLOS scenarios caused by the human and simulated body segments, a consistent and reproducible overestimation of distances was found. The reproducibility of these errors based on relative node and object positions suggests that systematic mitigation methods could significantly reduce errors, enabling more accurate and reproducible 3D human movement analysis.

Automatic Detection of Magnetic Disturbances in Magnetic Inertial Measurement Unit Sensors Based on Recurrent Neural Networks.

This paper proposes a new methodology for the automatic detection of magnetic disturbances from magnetic inertial measurement unit (MIMU) sensors based on deep learning. The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way. The model was trained and tested using 15 folds, which considered independence in sensor, disturbance direction, and signal type. On average, the network can adequately detect the disturbances in 98% of the cases, which represents a significant improvement over current threshold-based detection algorithms.

Feasibility of Shoulder Kinematics Assessment Using Magnetic Inertial Measurement Units in Hemiplegic Patients after Stroke: A Pilot Study

Scapulothoracic movements are altered after stroke, with resulting shoulder dysfunction. The scapulohumeral rhythm (SHR) is complex and poorly studied. Magnetic inertial measurement units (MIMUs) allow a rapid and accurate analysis of shoulder kinematics. MIMUs were used to assess the SHR during active shoulder flexion and abduction of over 60°. SHR values obtained from the hemiplegic shoulders of stroke patients (n = 7) were compared with those from healthy controls (n = 25) and correlated with clinical–functional measurements. The impairment of paretic arms was assessed using the Fugl-Meyer Assessment (FMA). We found that in paretic shoulders, the scapular tilt was significantly lower at maximal arm flexion and at 60° and 90° of arm abduction. On the paretic side, the SHR was also consistently lower for all measured arm movements. The FMA was correlated with the scapular anterior–posterior tilt at 60° and 90° of shoulder abduction (Rho = 0.847, p = 0.016, and Rho = 0.757, p = 0.049, respectively). This pilot study demonstrates the feasibility of MIMUs in assessing SHR in stroke patients and confirms previous findings on scapular dysfunction in stroke patients.

Integrated UWB/MIMU Sensor System for Position Estimation towards an Accurate Analysis of Human Movement: A Technical Review.

Integrated Ultra-wideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensor systems have been gaining popularity for pedestrian tracking and indoor localization applications, mainly due to their complementary error characteristics that can be exploited to achieve higher accuracies via a data fusion approach. These integrated sensor systems have the potential for improving the ambulatory 3D analysis of human movement (estimating 3D kinematics of body segments and joints) over systems using only on-body MIMUs. For this, high accuracy is required in the estimation of the relative positions of all on-body integrated UWB/MIMU sensor modules. So far, these integrated UWB/MIMU sensors have not been reported to have been applied for full-body ambulatory 3D analysis of human movement. Also, no review articles have been found that have analyzed and summarized the methods integrating UWB and MIMU sensors for on-body applications. Therefore, a comprehensive analysis of this technology is essential to identify its potential for application in 3D analysis of human movement. This article thus aims to provide such a comprehensive analysis through a structured technical review of the methods integrating UWB and MIMU sensors for accurate position estimation in the context of the application for 3D analysis of human movement. The methods used for integration are all summarized along with the accuracies that are reported in the reviewed articles. In addition, the gaps that are required to be addressed for making this system applicable for the 3D analysis of human movement are discussed.

Benchmarking Dataset of Signals from a Commercial MEMS Magnetic-Angular Rate-Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion.

In this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic-angular rate-gravity (MARG) sensor module (also known as magnetic inertial measurement unit, MIMU) for the evaluation of MARG orientation estimation algorithms. The dataset contains 30 files resulting from different volunteer subjects executing manipulations of the MARG in areas with and without magnetic distortion. Each file also contains reference ("ground truth") MARG orientations (as quaternions) determined by an optical motion capture system during the recording of the MARG signals. The creation of FIUMARGDB responds to the increasing need for the objective comparison of the performance of MARG orientation estimation algorithms, using the same inputs (accelerometer, gyroscope, and magnetometer signals) recorded under varied circumstances, as MARG modules hold great promise for human motion tracking applications. This dataset specifically addresses the need to study and manage the degradation of orientation estimates that occur when MARGs operate in regions with known magnetic field distortions. To our knowledge, no other dataset with these characteristics is currently available. FIUMARGDB can be accessed through the URL indicated in the conclusions section. It is our hope that the availability of this dataset will lead to the development of orientation estimation algorithms that are more resilient to magnetic distortions, for the benefit of fields as diverse as human-computer interaction, kinesiology, motor rehabilitation, etc.

Testing the Use of Advanced Upper Limb Prostheses: Towards Quantifying the Movement Quality with Inertial-Magnetic Measurement Units

Background: A thorough assessment of upper limb prostheses could help facilitate their transfer from scientific developments into the daily lives of users. Ideally, routine clinical testing would include assessments of upper limb function using motion-capturing technology. This is particularly relevant for the state-of-the-art upper limb prostheses. Methods: We designed a test based on an activity of daily life (“tray-task”) which could be completed outside the laboratory, and developed a set of outcome measures aimed at characterizing the movement quality. For this purpose, kinematics of the thorax and the humerus were captured with an inertial–magnetic measurement unit (IMMU) motion-capture system. Six prosthesis users and ten able-bodied participants were recruited to test the feasibility of the proposed assessment procedure and to evaluate the outcome variables. Results: All participants completed the test either at home or in our lab. The prosthesis users needed more time to complete the task and showed a larger range of motion in the thoracic flexion and a smaller range of motion in the humeral elevation, compared to the able-bodied participants. Furthermore, the prosthesis users’ movements were less smooth and characterized by less stable coordination patterns between the humerus and thorax. Conclusion: A new test method and associated outcome variables have been proposed.

A Flexible Sensor and MIMU-Based Multisensor Wearable System for Human Motion Analysis

Motivation: Magnetic–inertial measurement units (MIMUs) and flexible sensors are widely used in the wearable measurement system for human motion monitoring, clinical gait detection, and robotics motion control. However, MIMUs demonstrate measurement error due to magnetic disturbance in the indoor environment, and flexible sensors usually have low performance on linearity and accuracy. Objective: This article is intended to eliminate the low-accuracy problem caused by magnetic disturbances and improve the measurement accuracy of MIMU–flexible-sensor-based wearable systems. Approach: 1) a three-stage real-time adaptive anti-disturbance data fusion (RT-ADF) algorithm is proposed, which contains an anti-disturbance filter based on a double Mahony filter along with a state observer, a signal holder for sensors’ data transmit synchronously, and a data fusion based on an adaptive Kalman filter; 2) the proposed algorithm is used and validated its performance on a designed MIMU–flexible sensor wearable system; and 3) ten groups of knee motions (flexion/extension), ten groups of hip motions (adduction/abduction), and ten groups of elbow motions (flexion/extension) have been done by seven subjects in the experiments. Main Results: The designed multisensor wearable system based on the presented data fusion algorithm demonstrates a high-accuracy performance under the magnetic disturbance environment, and the maximum root mean square error (RMSE) of the measured continuous 3-D motion angle of the knee, hip, and elbow cross all the experiments was 1.23°, 1.15°, and 3.67° for each axis.

Center of mass velocity comparison using a whole body magnetic inertial measurement unit system and force platforms in well trained sprinters in straight-line and curve sprinting

BackgroundSprint performance can be characterized through the centre of mass (COM) velocity over time. In-field computation of the COM is key in sprint training. Research questionTo compare the stance-averaged COM velocity computation from a Magneto-Inertial Measurement Units (MIMU) to a reference system: force platforms (FP), over the early acceleration phase in both straight and curve sprinting. MethodsNineteen experienced-to-elite track sprinters performed 1 maximal sprint on both the straight and the curve (radius = 41.58 m) in a randomized order. Utilizing a MIMU-based system (Xsens MVN Link) and compared to FP (Kistler), COM velocity was computed with both systems. Averaged stance-by-stance COM velocity over straight-line and curve sprinting following the vertical axis (respectively VzMIMU and VzFP) and the norm of the two axes lying on the horizontal plane: x and y, approximately anteroposterior and mediolateral (respectively VxyMIMU and VxyFP) over the starting-blocks (SB) and initial acceleration (IA – composed out of the first four stances following the SB) were compared using mean bias, 95 % limits of agreements and Pearson’s correlation coefficients. Results148 stances were analyzed. VxyMIMU mean bias was comprised between 0.26 % and 2.03 % (expressed in % with respect to the FP) for SB, 5.63 % and 7.29 % over IA respectively on the straight and the curve. Pearson’s correlation coefficients ranged between 0.943 and 0.990 for Vxy, 0.423 and 0.938 for Vz. On the other hand, VzMIMU mean bias ranged between 2.33 % and 4.69 % for SB, between 1.44 % and 19.95 % over IA respectively on the straight and the curve SignificanceThe present findings suggest that the MIMU-based system tested slightly underestimated VxyMIMU, though within narrow limits which supports its utilization. On the other hand, VzMIMU computation in sprint running is not fully mature yet. Therefore, this MIMU-based system represents an interesting device for in-fieldVxyMIMU computation either for straight-line and curve sprinting.

Reliability of wearable sensors-based parameters for the assessment of knee stability.

Anterior cruciate ligament (ACL) rupture represents one of the most recurrent knee injuries in soccer players. To allow a safe return to sport after ACL reconstruction, standardised and reliable procedures/criteria are needed. In this context, wearable sensors are gaining momentum as they allow obtaining objective information during sport-specific and in-the-field tasks. This paper aims at proposing a sensor-based protocol for the assessment of knee stability and at quantifying its reliability. Seventeen soccer players performed a single leg squat and a cross over hop test. Each participant was equipped with two magnetic-inertial measurement units located on the tibia and foot. Parameters related to the knee stability were obtained from linear acceleration and angular velocity signals. The intraclass correlation coefficient (ICC) and minimum detectable change (MDC) were calculated to evaluate each parameter reliability. The ICC ranged from 0.29 to 0.84 according to the considered parameter. Specifically, angular velocity-based parameters proved to be more reliable than acceleration-based counterparts, particularly in the cross over hop test (average ICC values of 0.46 and 0.63 for acceleration- and angular velocity-based parameters, respectively). An exception was represented, in the single leg squat, by parameters extracted from the acceleration trajectory on the tibial transverse plane (0.60≤ICC≤0.76), which can be considered as promising candidates for ACL injury risk assessment. Overall, greater ICC values were found for the dominant limb, with respect to the non-dominant one (average ICC: 0.64 and 0.53, respectively). Interestingly, this between-limb difference in variability was not always mirrored by LSI results. MDC values provide useful information in the perspective of applying the proposed protocol on athletes with ACL reconstruction. Thus, The outcome of this study sets the basis for the definition of reliable and objective criteria for return to sport clearance after ACL injury.

Pedestrian Inertial Navigation Based on Full-Phase Constraints of Lower Limb Kinematics

Due to the low-cost magnetic/inertial measurement unit (MIMU) is easily affected by noise, the positioning result is inaccurate. In this study, to improve the precision of pedestrian positioning, a lower limb constraint model is constructed based on the gait characteristics and biomechanical relationship. Dual MIMUs are strapped to the foot and shank of the same leg to form a pedestrian inertial navigation systems (PINS). Firstly, taking the MIMU mounted on the shank as the reference point, a new velocity constraint relation is established. Combined with kinematics and Coriolis theorem, the velocity of the reference point is calculated by two MIMUs, respectively. Then, the two calculations are optimized via a Kalman filter. Secondly, a geometric mode is built owing to the position relationship between the ankle and two MIMUs. Using cosine theorem, a correction method with complementary constraints is established. In addition, different from the traditional zero-velocity update (ZUPT), the velocity and geometric constraints are applied throughout the gait cycle. Consequently, the proposed constraint methods can effectively suppress the position drift. To verify the effectiveness of the proposed algorithm, several experiments with different trajectories and velocities are designed. The results show that the trajectories obtained by the proposed methods are closer to the actual situations.

Racket orientation angle differences between accurate and inaccurate squash shots, as determined by a racket embedded magnetic-inertial measurement unit

ABSTRACT Ascertaining how racket orientation angle differences at ball-impact influence the accuracy of different squash strokes could assist player skill development and possibly reduce the number of unforced errors hit within a match. The purpose of this study was to identify differences in racket orientation angles of accurate and inaccurate forehand and backhand drive, volley and drop shots. A magnetic-inertial measurement unit embedded in a racket output orientation angles of twelve male junior players, with five accurate and five inaccurate shots per player per stroke analysed. Paired samples t-tests revealed that inaccurate backhand drop shots exhibited significantly (p < 0.05) less racket roll angle (racket face less open) at impact than accurate shots, indicating this parameter was a determining factor in the accuracy of this stroke. Racket orientation angle differences between accurate and inaccurate shots of the remaining strokes were too small to be used to distinguish shot accuracy. There was significantly greater variability in racket orientation angles during inaccurate forehand drop and backhand drive shots compared to accurate shots. These findings demonstrate how racket orientation angle differences at ball-impact can influence the accuracy of shots and highlights the need for consistent racket orientations to allow for an accurate shot.

An Innovative Sensor Fusion Algorithm for Motion Tracking With On-Line Bias Compensation: Application to Joint Angles Estimation in Yoga

Magnetic Inertial Measurement Units (MIMUs) represent an increasingly used technology in the field of human motion. However, their applications are strongly affected by magnetic disturbances and poor calibration, which could determine inaccurate attitude estimations. Thus, Inertial Measurement Units (IMUs), relying exclusively on accelerometers and gyroscopes, could be used as an alternative to MIMUs for motion tracking, avoiding the negative effect of magnetic disturbances. Unfortunately, gyroscope signals are characterized by several error sources, among which gyroscope bias, whose variations due to environmental factors and temporal instability strongly affect the attitude estimation accuracy. Aim of the present work is to propose a novel sensor fusion algorithm for IMU-based applications that embodies an adaptive on-line bias capture module. The accuracy of the proposed filter was tested on ten expert yoga practitioners during the execution of a sun salutation sequence. The right upper limb joint angles were estimated and their accuracy assessed in terms of Mean Absolute Error (MAE) and Pearson’s correlation coefficient by comparison with an optoelectronic motion capture system. The achieved worst-case accuracy was 5.59, 6.75 and 3.49 degrees for the wrist, elbow and shoulder joints respectively. The accuracy of the algorithm is further confirmed by the high values of the Pearson’s correlation coefficients between IMUs and OMC angles, which were greater than 0.91, 0.92 and 0.98 respectively for the three considered joints. The proposed algorithm can thus be considered as a promising tool for attitude estimation in those contexts when magnetic interferences cannot be monitored and, thus, adequately compensated.

Automatically Determining Lumbar Load during Physically Demanding Work: A Validation Study.

A sensor-based system using inertial magnetic measurement units and surface electromyography is suitable for objectively and automatically monitoring the lumbar load during physically demanding work. The validity and usability of this system in the uncontrolled real-life working environment of physically active workers are still unknown. The objective of this study was to test the discriminant validity of an artificial neural network-based method for load assessment during actual work. Nine physically active workers performed work-related tasks while wearing the sensor system. The main measure representing lumbar load was the net moment around the L5/S1 intervertebral body, estimated using a method that was based on artificial neural network and perceived workload. The mean differences (MDs) were tested using a paired t-test. During heavy tasks, the net moment (MD = 64.3 ± 13.5%, p = 0.028) and the perceived workload (MD = 5.1 ± 2.1, p < 0.001) observed were significantly higher than during the light tasks. The lumbar load had significantly higher variances during the dynamic tasks (MD = 33.5 ± 36.8%, p = 0.026) and the perceived workload was significantly higher (MD = 2.2 ± 1.5, p = 0.002) than during static tasks. It was concluded that the validity of this sensor-based system was supported because the differences in the lumbar load were consistent with the perceived intensity levels and character of the work tasks.

Simultaneous End User Calibration of Multiple Magnetic Inertial Measurement Units With Associated Uncertainty

A fast method to simultaneously calibrate multiple MEMS Magnetic Inertial Measurement Units (MIMUs) accurately in the field is needed in many application areas. The MEMS MIMUs require calibration of systematic errors of bias, sensitivity, non-orthogonality and misalignment, which vary with temperature and use. Even after calibration, the sensors undergo stochastic errors in static and dynamic conditions and thus uncertainty of output must also be modeled. We propose a method for easy and fast calibration of multiple MIMUs together, while mounted on a single platform. The precise alignment of sensors is not assumed. Our method calibrates both fixed array of MEMS MIMUs or many independent MIMUs simultaneously using kinematic constraints. The novelty of our approach is that the uncertainty of sensors output is also learned as part of our model. Compared with existing state-of-art methods, our algorithm gives more consistent readings of all MIMUs and our framework also predicts the associated uncertainty of the sensor output. The uncertainty prediction of individual sensors is particularly helpful in the sensor fusion.

Magnetometer Robust Deep Human Pose Regression With Uncertainty Prediction Using Sparse Body Worn Magnetic Inertial Measurement Units

We propose a deep learning based framework that learns data-driven temporal priors to perform 3D human pose estimation from six body worn Magnetic Inertial Measurement units sensors. Our work estimates 3D human pose with associated uncertainty from sparse body worn sensors. We derive and implement a 3D angle representation that eliminates yaw angle (or magnetometer dependence) and show that 3D human pose is still obtained from this reduced representation, but with enhanced uncertainty. We do not use kinematic acceleration as input and show that it improves the generalization to real sensor data from different subjects as well as accuracy. Our framework is based on Bi-directional recurrent autoencoder. A sliding window is used at inference time, instead of full sequence (offline mode). The major contribution of our research is that 3D human pose is predicted from sparse sensors with a well calibrated uncertainty which is correlated with ambiguity and actual errors. We have demonstrated our results on two real sensor datasets; DIP-IMU and Total capture and have come up with state-of-art accuracy. Our work confirms that the main limitation of sparse sensor based 3D human pose prediction is the lack of temporal priors. Therefore fine-tuning on a small synthetic training set of target domain, improves the accuracy.

Effect of Functional Electrical Stimulation of the Gluteus Medius during Gait in Patients following a Stroke.

Many stroke patients rely on cane or ankle-foot orthosis during gait rehabilitation. The purpose of this study was to investigate the immediate effect of functional electrical stimulation (FES) to the gluteus medius (GMed) and tibialis anterior (TA) on gait performance in stroke patients, including those who needed assistive devices. Fourteen stroke patients were enrolled in this study (mean poststroke duration: 194.9 ± 189.6 d; mean age: 72.8 ± 10.7 y). Participants walked 14 m at a comfortable velocity with and without FES to the GMed and TA. After an adaptation period, lower-limb motion was measured using magnetic inertial measurement units attached to the pelvis and the lower limb of the affected side. Motion range of angle of the affected thigh and shank segments in the sagittal plane, motion range of the affected hip and knee extension-flexion angle, step time, and stride time were calculated from inertial measurement units during the middle ten walking strides. Gait velocity, cadence, and stride length were also calculated. These gait indicators, both with and without FES, were compared. Gait velocity was significantly faster with FES (p = 0.035). Similarly, stride length and motion range of the shank of the affected side were significantly greater with FES (stride length: p = 0.018; motion range of the shank: p = 0.026). Meanwhile, cadence showed no significant difference (p = 0.238) in gait with or without FES. Similarly, range of motion of the affected hip joint, knee joint, and thigh did not differ significantly depending on FES condition (p = 0.115‐0.529). FES to the GMed and TA during gait produced an improvement in gait velocity, stride length, and motion range of the shank. Our results will allow therapists to use FES on stroke patients with varying conditions.

Changes In Running Mechanics During A Typical Interval Training On The Track Measured With IMUs

An 8 x 400 meter interval training is often performed with the aim of improving aerobic fitness. Besides being physiological and physically challenging this type of training is also mechanically demanding. As such it could have potential negative effects on running mechanics. Inertial magnetic measurement units (IMUs) allow for continuous measurement of running mechanics during this type of training. PURPOSE: To investigate changes in running mechanics during an 8 x 400 meter interval training on the athletic track using IMUs. METHODS: 5 trained athletes (4M 1F, 25.4±7.9 years, 185.6±8.3 cm, 69.2±12.7 kg) ran 8 x 400 meters on the athletic track. They were paced to run each 400 meter at 5 km race pace with half of the time run as rest. Eight IMUs (240 Hz) were placed at the feet, tibia, upper legs, sacrum and sternum. Accelerometer data and sensor orientation were used to calculate the following parameters using custom code after calibration trials: Hip, knee and ankle angle at Initial Contact (IC), knee angle at Midstance (MST) and Midswing (MSW), peak tibial and sacral acceleration (PTA, PSA), and centre of mass (COM) displacement. Parameters were calculated for both straights of the 2nd, 4th, 6th and 8th 400 meters. Borg scale (0-10) was asked after every bout for perceived exertion. Paired sample t-tests were used to test for statistical differences between the 2nd and 8th bout. RESULTS: Table 1 CONCLUSIONS: Running mechanics (mainly ankle and knee mechanics and tibial impact) changed over the course of a typical interval training, putting runners at higher risk with increasing bouts. This indicates that this type of training is not only physiological and physically demanding but puts increasing mechanical stress on the body. These results suggest caution should be used among athletes returning from an overuse injury.

Estimating the Instantaneous Screw Axis and the Screw Axis Invariant Descriptor of Motion by Means of Inertial Sensors: An Experimental Study with a Mechanical Hinge Joint and Comparison to the Optoelectronic System.

The motion of a rigid body can be represented by the instantaneous screw axis (ISA, also known as the helical axis). Recently, an invariant representation of motion based on the ISA, namely, the screw axis invariant descriptor (SAID), was proposed in the literature. The SAID consists of six scalar features that are independent from the coordinate system chosen to represent the motion. This method proved its usefulness in robotics; however, a high sensitivity to noise was observed. This paper aims to explore the performance of inertial sensors for the estimation of the ISA and the SAID for a simple experimental setup based on a hinge joint. The free swing motion of the mechanical hinge was concurrently recorded by a marker-based optoelectronic system (OS) and two magnetic inertial measurement units (MIMUs). The ISA estimated by the MIMU was more precise, while the OS was more accurate. The mean angular error was ≈2.2° for the OS and was ≈4.4° for the MIMU, while the mean standard deviation was ≈2.3° for the OS and was ≈0.2° for the MIMU. The SAID features based on angular velocity were better estimated by the MIMU, while the features based on translational velocity were better estimated by the OS. Therefore, a combination of both measurements systems is recommended to accurately estimate the complete SAID.

Validity of Measurement for Trailing Limb Angle and Propulsion Force during Gait Using a Magnetic Inertial Measurement Unit.

Propulsion force and trailing limb angle (TLA) are meaningful indicators for evaluating quality of gait. This study examined the validity of measurement for TLA and propulsion force during various gait conditions using magnetic inertial measurement units (IMU), based on measurements using a three-dimensional motion analysis system and a force platform. Eighteen healthy males (mean age 25.2 ± 3.2 years, body height 1.70 ± 0.06 m) walked with and without trunk fluctuation at preferred, slow, and fast velocities. IMU were fixed on the thorax, lumbar spine, and right thigh and shank. IMU calculated the acceleration and tilt angles in a global coordinate system. TLA, consisting of a line connecting the hip joint with the ankle joint, and the laboratory's vertical axis at late stance in the sagittal plane, was calculated from thigh and shank segment angles obtained by IMU, and coordinate data from the motion analysis system. Propulsion force was estimated by the increment of velocity calculated from anterior acceleration measured by IMU fixed on the thorax and lumbar spine, and normalized impulse of the anterior component of ground reaction force (AGRF) during late stance. Similarity of TLA measured by IMU and the motion analysis system was tested by the coefficient of multiple correlation (CMC), intraclass correlation coefficient (ICC), and root mean square (RMS) of measurement error. Relationships between normalized impulse of AGRF and increments of velocity, as measured by IMU, were tested using correlation analysis. CMC of TLA was 0.956–0.959. ICC between peak TLAs was 0.831–0.876 (p < 0.001), and RMS of error was 1.42°–1.92°. Velocity increment calculated from acceleration on the lumbar region showed strong correlations with normalized impulse of AGRF (r = 0.755–0.892, p < 0.001). These results indicated a high validity of estimation of TLA and propulsion force by IMU during various gait conditions; these methods would be useful for best clinical practice.

A Novel Adaptive Kalman Filtering Approach to Human Motion Tracking With Magnetic-Inertial Sensors

This article presents an adaptive Kalman filtering approach to estimate the orientation of human body segments by using magnetic-inertial measurement units (MIMUs) with three-axis gyroscope, accelerometer, and magnetometer. In order to mitigate the negative impact of the external accelerations and ferromagnetic disturbances, the disturbances are modeled by first-order Gauss–Markov processes, and two parallel adaptive Kalman filters containing disturbance models are implemented to eliminate the disturbances. An adaptive factor is introduced to adjust the process noise covariance matrix to compensate for modeling errors induced by unmodeled gyroscope biases and the erroneous process noise covariance matrices. Furthermore, the outliers are checked and discarded by hypothesis tests on the innovation, and the measurement is rejected when the innovation exceeds a given confidence interval. Then, the estimated gravity acceleration and geomagnetic field are used to calculate the orientation quaternion using triaxial attitude determination algorithm. Finally, experiments under different motion scenarios are carried out to evaluate the effectiveness of the proposed method, and the performance of the proposed method is discussed in comparison with existing ones. A forward kinematics three-dimensional (3-D) human motion reconstruction method is proposed to drive the human skeleton model to reproduce human motion with a visual interface based on ROS platform.